# sample1 = [row['CFU'], row['unk']]
# sample2 = [row['poly'], row['int']]
# print(gene_name, stats.ttest_ind(sample1, sample2).pvalue)
cfu1 = list(diff_exp_genes["CFU"].values)
poly1 = list(diff_exp_genes["poly"].values)
int1 = list(diff_exp_genes["int"].values)
unk1 = list(diff_exp_genes["unk"].values)
gene_name1 = list(diff_exp_genes.index.values)
l = len(gene_name1)
# print(l)
# print(gene_name1)
sig_de_genes = []
for i in range(l):
early = [cfu1[i], unk1[i]]
late = [poly1[i], int1[i]]
t, p = (sp.ttest_rel(early, late))
if p < 0.05:
sig_de_genes.append(gene_name1[i])
# print(i)
print(sig_de_genes)
labels = list(kmeans.labels_)
genes = list(df_data.index.values)
goi_index = genes.index(sys.argv[2])
goi_cluster = labels[goi_index]
related_genes = []
for i, gene in enumerate(genes):
if labels[i] == goi_cluster:
related_genes.append(gene)
print(related_genes)
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy.cluster.hierarchy import linkage, dendrogram, leaves_list
import scipy.stats as sp
hema = open(sys.argv[1])
df = pd.read_csv(hema, sep="\t", index_col=0)
diff_exp_high = (((df['CFU'] + df['unk']) / 2) /
((df['poly'] + df['int']) / 2)) >= 2
diff_exp_low = (((df['CFU'] + df['unk']) / 2) /
((df['poly'] + df['int']) / 2)) <= 0.5
diff_exp_genes = df[
diff_exp_high |
diff_exp_low] #it prints out a whole dataframe, we just got the genes that have 2fold exp, we will now test
for gene_name, row in diff_exp_genes.iterrows():
sample1 = [row['CFU'], row['unk']]
sample2 = [row['poly'], row['int']]
# print(gene_name,sp.ttest_rel(sample1, sample2).pvalue)
if sp.ttest_rel(sample1, sample2).pvalue <= 0.05:
print(gene_name, sp.ttest_rel(sample1, sample2).pvalue)
# for gene, row in diff_exp_genes.iterrows():
# diff_high = (((df['CFU']+df['unk'])/2)/((df['poly']+df['int'])/2))
# diff_low = (((df['CFU']+df['unk'])/2)/((df['poly']+df['int'])/2))
#